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analytics</title> <meta name="description" content="Search results for: drilldown analytics"> <meta name="keywords" content="drilldown analytics"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="drilldown analytics"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 370</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: drilldown analytics</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">370</span> Navigating Government Finance Statistics: Effortless Retrieval and Comparative Analysis through Data Science and Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kwaku%20Damoah">Kwaku Damoah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20science" title="data science">data science</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20wrangling" title=" data wrangling"> data wrangling</a>, <a href="https://publications.waset.org/abstracts/search?q=drilldown%20analytics" title=" drilldown analytics"> drilldown analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20finance%20statistics" title=" government finance statistics"> government finance statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20network%20classification" title=" hierarchical network classification"> hierarchical network classification</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20application." title=" web application."> web application.</a> </p> <a href="https://publications.waset.org/abstracts/179211/navigating-government-finance-statistics-effortless-retrieval-and-comparative-analysis-through-data-science-and-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179211.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">70</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">369</span> Leveraging Learning Analytics to Inform Learning Design in Higher Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mingming%20Jiang">Mingming Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This literature review aims to offer an overview of existing research on learning analytics and learning design, the alignment between the two, and how learning analytics has been leveraged to inform learning design in higher education. Current research suggests a need to create more alignment and integration between learning analytics and learning design in order to not only ground learning analytics on learning sciences but also enable data-driven decisions in learning design to improve learning outcomes. In addition, multiple conceptual frameworks have been proposed to enhance the synergy and alignment between learning analytics and learning design. Future research should explore this synergy further in the unique context of higher education, identifying learning analytics metrics in higher education that can offer insight into learning processes, evaluating the effect of learning analytics outcomes on learning design decision-making in higher education, and designing learning environments in higher education that make the capturing and deployment of learning analytics outcomes more efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title="learning analytics">learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20design" title=" learning design"> learning design</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20higher%20education" title=" big data in higher education"> big data in higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning%20environments" title=" online learning environments"> online learning environments</a> </p> <a href="https://publications.waset.org/abstracts/149822/leveraging-learning-analytics-to-inform-learning-design-in-higher-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">172</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">368</span> A Machine Learning Decision Support Framework for Industrial Engineering Purposes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anli%20Du%20Preez">Anli Du Preez</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Bekker"> James Bekker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Data%20analytics" title="Data analytics">Data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=Industrial%20engineering" title=" Industrial engineering"> Industrial engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=Machine%20learning" title=" Machine learning"> Machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Value%20creation" title=" Value creation"> Value creation</a> </p> <a href="https://publications.waset.org/abstracts/116912/a-machine-learning-decision-support-framework-for-industrial-engineering-purposes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">367</span> Applications of Big Data in Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Kalota">Faisal Kalota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20education" title=" big data in education"> big data in education</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop "> Hadoop </a> </p> <a href="https://publications.waset.org/abstracts/27525/applications-of-big-data-in-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">366</span> A Study on Big Data Analytics, Applications and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chhavi%20Rana">Chhavi Rana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=review" title=" review"> review</a> </p> <a href="https://publications.waset.org/abstracts/162947/a-study-on-big-data-analytics-applications-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162947.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">83</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">365</span> A Study on Big Data Analytics, Applications, and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chhavi%20Rana">Chhavi Rana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=review" title=" review"> review</a> </p> <a href="https://publications.waset.org/abstracts/150593/a-study-on-big-data-analytics-applications-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">364</span> High Performance Computing and Big Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Branci%20Sarra">Branci Sarra</a>, <a href="https://publications.waset.org/abstracts/search?q=Branci%20Saadia"> Branci Saadia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20performance%20computing" title="high performance computing">high performance computing</a>, <a href="https://publications.waset.org/abstracts/search?q=HPC" title=" HPC"> HPC</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/15079/high-performance-computing-and-big-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">520</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">363</span> Energy Efficiency and Sustainability Analytics for Reducing Carbon Emissions in Oil Refineries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaurav%20Kumar%20Sinha">Gaurav Kumar Sinha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title="energy efficiency">energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability%20analytics" title=" sustainability analytics"> sustainability analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=carbon%20emissions" title=" carbon emissions"> carbon emissions</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20refineries" title=" oil refineries"> oil refineries</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20optimization" title=" process optimization"> process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse%20gas%20reduction" title=" greenhouse gas reduction"> greenhouse gas reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20performance" title=" environmental performance"> environmental performance</a> </p> <a href="https://publications.waset.org/abstracts/187014/energy-efficiency-and-sustainability-analytics-for-reducing-carbon-emissions-in-oil-refineries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">31</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">362</span> Social Semantic Web-Based Analytics Approach to Support Lifelong Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Halimi">Khaled Halimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassina%20Seridi-Bouchelaghem"> Hassina Seridi-Bouchelaghem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to describe how learning analytics approaches based on social semantic web techniques can be applied to enhance the lifelong learning experiences in a connectivist perspective. For this reason, a prototype of a system called <em>SoLearn</em> (Social Learning Environment) that supports this approach. We observed and studied literature related to lifelong learning systems, social semantic web and ontologies, connectivism theory, learning analytics approaches and reviewed implemented systems based on these fields to extract and draw conclusions about necessary features for enhancing the lifelong learning process. The semantic analytics of learning can be used for viewing, studying and analysing the massive data generated by learners, which helps them to understand through recommendations, charts and figures their learning and behaviour, and to detect where they have weaknesses or limitations. This paper emphasises that implementing a learning analytics approach based on social semantic web representations can enhance the learning process. From one hand, the analysis process leverages the meaning expressed by semantics presented in the ontology (relationships between concepts). From the other hand, the analysis process exploits the discovery of new knowledge by means of inferring mechanism of the semantic web. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=connectivism" title="connectivism">connectivism</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=lifelong%20learning" title=" lifelong learning"> lifelong learning</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20semantic%20web" title=" social semantic web"> social semantic web</a> </p> <a href="https://publications.waset.org/abstracts/100850/social-semantic-web-based-analytics-approach-to-support-lifelong-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100850.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">361</span> Visual Analytics in K 12 Education: Emerging Dimensions of Complexity </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linnea%20Stenliden">Linnea Stenliden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors by Latour. The learning conditions are found to be distinguished by broad complexity characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20reasoning" title="analytical reasoning">analytical reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20use" title=" data use"> data use</a>, <a href="https://publications.waset.org/abstracts/search?q=problem%20space" title=" problem space"> problem space</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20analytics" title=" visual analytics"> visual analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20storytelling" title=" visual storytelling"> visual storytelling</a>, <a href="https://publications.waset.org/abstracts/search?q=translation" title=" translation"> translation</a> </p> <a href="https://publications.waset.org/abstracts/17440/visual-analytics-in-k-12-education-emerging-dimensions-of-complexity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17440.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">360</span> A Predictive Analytics Approach to Project Management: Reducing Project Failures in Web and Software Development Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tazeen%20Fatima">Tazeen Fatima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Use of project management in web & software development projects is very significant. It has been observed that even with the application of effective project management, projects usually do not complete their lifecycle and fail. To minimize these failures, key performance indicators have been introduced in previous studies to counter project failures. However, there are always gaps and problems in the KPIs identified. Despite of incessant efforts at technical and managerial levels, projects still fail. There is no substantial approach to identify and avoid these failures in the very beginning of the project lifecycle. In this study, we aim to answer these research problems by analyzing the concept of predictive analytics which is a specialized technology and is very easy to use in this era of computation. Project organizations can use data gathering, compute power, and modern tools to render efficient Predictions. The research aims to identify such a predictive analytics approach. The core objective of the study was to reduce failures and introduce effective implementation of project management principles. Existing predictive analytics methodologies, tools and solution providers were also analyzed. Relevant data was gathered from projects and was analyzed via predictive techniques to make predictions well advance in time to render effective project management in web & software development industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=project%20management" title="project management">project management</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics" title=" predictive analytics"> predictive analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics%20methodology" title=" predictive analytics methodology"> predictive analytics methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20failures" title=" project failures"> project failures</a> </p> <a href="https://publications.waset.org/abstracts/69625/a-predictive-analytics-approach-to-project-management-reducing-project-failures-in-web-and-software-development-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69625.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">359</span> Integrating Service Learning into a Business Analytics Course: A Comparative Investigation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gokhan%20Egilmez">Gokhan Egilmez</a>, <a href="https://publications.waset.org/abstracts/search?q=Erika%20Hatfield"> Erika Hatfield</a>, <a href="https://publications.waset.org/abstracts/search?q=Julie%20Turner"> Julie Turner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we investigated the impacts of service-learning integration on an undergraduate level business analytics course from multiple perspectives, including academic proficiency, community awareness, engagement, social responsibility, and reflection. We assessed the impact of the service-learning experience by using a survey developed primarily based on the literature review and secondarily on an ad hoc group of researchers. Then, we implemented the survey in two sections, where one of the sections was a control group. We compared the results of the empirical survey visually and statistically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title="business analytics">business analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20learning" title=" service learning"> service learning</a>, <a href="https://publications.waset.org/abstracts/search?q=experiential%20education" title=" experiential education"> experiential education</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=survey%20research" title=" survey research"> survey research</a> </p> <a href="https://publications.waset.org/abstracts/151733/integrating-service-learning-into-a-business-analytics-course-a-comparative-investigation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151733.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">358</span> Food Supply Chain Optimization: Achieving Cost Effectiveness Using Predictive Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jayant%20Kumar">Jayant Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aarcha%20Jayachandran%20Sasikala"> Aarcha Jayachandran Sasikala</a>, <a href="https://publications.waset.org/abstracts/search?q=Barry%20Adrian%20Shepherd"> Barry Adrian Shepherd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Public Distribution System is a flagship welfare programme of the Government of India with both historical and political significance. Targeted at lower sections of society,it is one of the largest supply chain networks in the world. There has been several studies by academics and planning commission about the effectiveness of the system. Our study focuses on applying predictive analytics to aid the central body to keep track of the problem of breach of service level agreement between the two echelons of food supply chain. Each shop breach is leading to a potential additional inventory carrying cost. Thus, through this study, we aim to show that aided with such analytics, the network can be made more cost effective. The methods we illustrate in this study are applicable to other commercial supply chains as well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PDS" title="PDS">PDS</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20effectiveness" title=" cost effectiveness"> cost effectiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=Karnataka" title=" Karnataka"> Karnataka</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20cost" title=" inventory cost"> inventory cost</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20level%20JEL%20classification%3A%20C53" title=" service level JEL classification: C53"> service level JEL classification: C53</a> </p> <a href="https://publications.waset.org/abstracts/21047/food-supply-chain-optimization-achieving-cost-effectiveness-using-predictive-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">533</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">357</span> Data Analytics in Hospitality Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tammy%20Wee">Tammy Wee</a>, <a href="https://publications.waset.org/abstracts/search?q=Detlev%20Remy"> Detlev Remy</a>, <a href="https://publications.waset.org/abstracts/search?q=Arif%20Perdana"> Arif Perdana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the recent years, data analytics has become the buzzword in the hospitality industry. The hospitality industry is another example of a data-rich industry that has yet fully benefited from the insights of data analytics. Effective use of data analytics can change how hotels operate, market and position themselves competitively in the hospitality industry. However, at the moment, the data obtained by individual hotels remain under-utilized. This research is a preliminary research on data analytics in the hospitality industry, using an in-depth face-to-face interview on one hotel as a start to a multi-level research. The main case study of this research, hotel A, is a chain brand of international hotel that has been systematically gathering and collecting data on its own customer for the past five years. The data collection points begin from the moment a guest book a room until the guest leave the hotel premises, which includes room reservation, spa booking, and catering. Although hotel A has been gathering data intelligence on its customer for some time, they have yet utilized the data to its fullest potential, and they are aware of their limitation as well as the potential of data analytics. Currently, the utilization of data analytics in hotel A is limited in the area of customer service improvement, namely to enhance the personalization of service for each individual customer. Hotel A is able to utilize the data to improve and enhance their service which in turn, encourage repeated customers. According to hotel A, 50% of their guests returned to their hotel, and 70% extended nights because of the personalized service. Apart from using the data analytics for enhancing customer service, hotel A also uses the data in marketing. Hotel A uses the data analytics to predict or forecast the change in consumer behavior and demand, by tracking their guest’s booking preference, payment preference and demand shift between properties. However, hotel A admitted that the data they have been collecting was not fully utilized due to two challenges. The first challenge of using data analytics in hotel A is the data is not clean. At the moment, the data collection of one guest profile is meaningful only for one department in the hotel but meaningless for another department. Cleaning up the data and getting standards correctly for usage by different departments are some of the main concerns of hotel A. The second challenge of using data analytics in hotel A is the non-integral internal system. At the moment, the internal system used by hotel A do not integrate with each other well, limiting the ability to collect data systematically. Hotel A is considering another system to replace the current one for more comprehensive data collection. Hotel proprietors recognized the potential of data analytics as reported in this research, however, the current challenges of implementing a system to collect data come with a cost. This research has identified the current utilization of data analytics and the challenges faced when it comes to implementing data analytics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title="data analytics">data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=hospitality%20industry" title=" hospitality industry"> hospitality industry</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20relationship%20management" title=" customer relationship management"> customer relationship management</a>, <a href="https://publications.waset.org/abstracts/search?q=hotel%20marketing" title=" hotel marketing"> hotel marketing</a> </p> <a href="https://publications.waset.org/abstracts/86574/data-analytics-in-hospitality-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86574.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">356</span> Web Page Design Optimisation Based on Segment Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varsha%20V.%20Rohini">Varsha V. Rohini</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20R.%20Shreya"> P. R. Shreya</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Renukadevi"> B. Renukadevi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the web analytics the information delivery and the web usage is optimized and the analysis of data is done. The analytics is the measurement, collection and analysis of webpage data. Page statistics and user metrics are the important factor in most of the web analytics tool. This is the limitation of the existing tools. It does not provide design inputs for the optimization of information. This paper aims at providing an extension for the scope of web analytics to provide analysis and statistics of each segment of a webpage. The number of click count is calculated and the concentration of links in a web page is obtained. Its user metrics are used to help in proper design of the displayed content in a webpage by Vision Based Page Segmentation (VIPS) algorithm. When the algorithm is applied on the web page it divides the entire web page into the visual block tree. The visual block tree generated will further divide the web page into visual blocks or segments which help us to understand the usage of each segment in a page and its content. The dynamic web pages and deep web pages are used to extend the scope of web page segment analytics. Space optimization concept is used with the help of the output obtained from the Vision Based Page Segmentation (VIPS) algorithm. This technique provides us the visibility of the user interaction with the WebPages and helps us to place the important links in the appropriate segments of the webpage and effectively manage space in a page and the concentration of links. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytics" title="analytics">analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20optimization" title=" design optimization"> design optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20block%20trees" title=" visual block trees"> visual block trees</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20based%20technology" title=" vision based technology"> vision based technology</a> </p> <a href="https://publications.waset.org/abstracts/56881/web-page-design-optimisation-based-on-segment-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56881.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">266</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">355</span> Collaborative Research between Malaysian and Australian Universities on Learning Analytics: Challenges and Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Tasir">Z. Tasir</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20N.%20Kew"> S. N. Kew</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20West"> D. West</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Abdullah"> Z. Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Toohey"> D. Toohey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research on Learning Analytics is progressively developing in the higher education field by concentrating on the process of students' learning. Therefore, a research project between Malaysian and Australian Universities was initiated in 2015 to look at the use of Learning Analytics to support the development of teaching practice. The focal point of this article is to discuss and share the experiences of Malaysian and Australian universities in the process of developing the collaborative research on Learning Analytics. Three aspects of this will be discussed: 1) Establishing an international research project and team members, 2) cross-cultural understandings, and 3) ways of working in relation to the practicalities of the project. This article is intended to benefit other researchers by highlighting the challenges as well as the strategies used in this project to ensure such collaborative research succeeds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20research%20project" title="academic research project">academic research project</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20research" title=" collaborative research"> collaborative research</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-cultural%20understanding" title=" cross-cultural understanding"> cross-cultural understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=international%20research%20project" title=" international research project"> international research project</a> </p> <a href="https://publications.waset.org/abstracts/48332/collaborative-research-between-malaysian-and-australian-universities-on-learning-analytics-challenges-and-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48332.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">242</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">354</span> A Case Study of Business Analytic Use in European Football: Analysis and Implications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20Schloesser">M. C. Schloesser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to explore the use and impact of business analytics in European football. Despite good evidence from other major sports leagues, research on this topic in Europe is currently very scarce. This research relies on expert interviews on the use and objective of business analytics. Along with revenue data over 16 seasons spanning from 2004/05 to 2019/20 from Manchester City FC, we conducted a time series analysis to detect a structural breakpoint on the different revenue streams, i.e., sponsorship and ticketing, after analytical tools have been implemented. We not only find that business analytics have indeed been applied at Manchester City FC and revenue increase is the main objective of their utilization but also that business analytics is indeed a good means to increase revenues if applied sufficiently. We can thereby support findings from other sports leagues. Consequently, professional sports organizations are advised to apply business analytics if they aim to increase revenues. This research has shown that analytical practices do, in fact, support revenue growth and help to work more efficiently. As the knowledge of analytical practices is very confidential and not publicly available, we had to select one club as a case study which can be considered a research limitation. Other practitioners should explore other clubs or leagues. Further, there are other factors that can lead to increased revenues that need to be considered. Additionally, sports organizations need resources to be able to apply and utilize business analytics. Consequently, findings might only apply to the top teams of the European football leagues. Nonetheless, this paper combines insights and results on usage, objectives, and impact of business analytics in European professional football and thereby fills a current research gap. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title="business analytics">business analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20interviews" title=" expert interviews"> expert interviews</a>, <a href="https://publications.waset.org/abstracts/search?q=revenue%20management" title=" revenue management"> revenue management</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title=" time series analysis"> time series analysis</a> </p> <a href="https://publications.waset.org/abstracts/167548/a-case-study-of-business-analytic-use-in-european-football-analysis-and-implications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167548.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">353</span> An Empirical Investigation of Big Data Analytics: The Financial Performance of Users versus Vendors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evisa%20Mitrou">Evisa Mitrou</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicholas%20Tsitsianis"> Nicholas Tsitsianis</a>, <a href="https://publications.waset.org/abstracts/search?q=Supriya%20Shinde"> Supriya Shinde</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affects the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BDA-clients" title="BDA-clients">BDA-clients</a>, <a href="https://publications.waset.org/abstracts/search?q=BDA-vendors" title=" BDA-vendors"> BDA-vendors</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20performance" title=" financial performance"> financial performance</a> </p> <a href="https://publications.waset.org/abstracts/152976/an-empirical-investigation-of-big-data-analytics-the-financial-performance-of-users-versus-vendors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152976.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">352</span> Data Management and Analytics for Intelligent Grid</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Julius%20P.%20Roy">G. Julius P. Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Saxena"> Prateek Saxena</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjeev%20Singh"> Sanjeev Singh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20management" title="data management">data management</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20data%20analytics" title=" energy data analytics"> energy data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20grid" title=" smart grid"> smart grid</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20utilities" title=" smart utilities "> smart utilities </a> </p> <a href="https://publications.waset.org/abstracts/27445/data-management-and-analytics-for-intelligent-grid" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27445.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">780</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">351</span> A Collaborative Problem Driven Approach to Design an HR Analytics Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Atif">L. Atif</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Rosenthal-Sabroux"> C. Rosenthal-Sabroux</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Grundstein"> M. Grundstein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The requirements engineering process is a crucial phase in the design of complex systems. The purpose of our research is to present a collaborative problem-driven requirements engineering approach that aims at improving the design of a Decision Support System as an Analytics application. This approach has been adopted to design a Human Resource management DSS. The Requirements Engineering process is presented as a series of guidelines for activities that must be implemented to assure that the final product satisfies end-users requirements and takes into account the limitations identified. For this, we know that a well-posed statement of the problem is “a problem whose crucial character arises from collectively produced estimation and a formulation found to be acceptable by all the parties”. Moreover, we know that DSSs were developed to help decision-makers solve their unstructured problems. So, we thus base our research off of the assumption that developing DSS, particularly for helping poorly structured or unstructured decisions, cannot be done without considering end-user decision problems, how to represent them collectively, decisions content, their meaning, and the decision-making process; thus, arise the field issues in a multidisciplinary perspective. Our approach addresses a problem-driven and collaborative approach to designing DSS technologies: It will reflect common end-user problems in the upstream design phase and in the downstream phase these problems will determine the design choices and potential technical solution. We will thus rely on a categorization of HR’s problems for a development mirroring the Analytics solution. This brings out a new data-driven DSS typology: Descriptive Analytics, Explicative or Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics. In our research, identifying the problem takes place with design of the solution, so, we would have to resort a significant transformations of representations associated with the HR Analytics application to build an increasingly detailed representation of the goal to be achieved. Here, the collective cognition is reflected in the establishment of transfer functions of representations during the whole of the design process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DSS" title="DSS">DSS</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20design" title=" collaborative design"> collaborative design</a>, <a href="https://publications.waset.org/abstracts/search?q=problem-driven%20requirements" title=" problem-driven requirements"> problem-driven requirements</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics%20application" title=" analytics application"> analytics application</a>, <a href="https://publications.waset.org/abstracts/search?q=HR%20decision%20making" title=" HR decision making"> HR decision making</a> </p> <a href="https://publications.waset.org/abstracts/61299/a-collaborative-problem-driven-approach-to-design-an-hr-analytics-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61299.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">295</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">350</span> From Theory to Practice: Harnessing Mathematical and Statistical Sciences in Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ullah">Zahid Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Atlas%20Khan"> Atlas Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title="data analytics">data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20sciences" title=" mathematical sciences"> mathematical sciences</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=interdisciplinary%20collaboration" title=" interdisciplinary collaboration"> interdisciplinary collaboration</a>, <a href="https://publications.waset.org/abstracts/search?q=practical%20applications" title=" practical applications"> practical applications</a> </p> <a href="https://publications.waset.org/abstracts/167377/from-theory-to-practice-harnessing-mathematical-and-statistical-sciences-in-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">349</span> Big Data and Analytics in Higher Education: An Assessment of Its Status, Relevance and Future in the Republic of the Philippines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Byron%20Joseph%20A.%20Hallar">Byron Joseph A. Hallar</a>, <a href="https://publications.waset.org/abstracts/search?q=Annjeannette%20Alain%20D.%20Galang"> Annjeannette Alain D. Galang</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Visitacion%20N.%20Gumabay"> Maria Visitacion N. Gumabay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20education" title=" higher education"> higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=republic%20of%20the%20philippines" title=" republic of the philippines"> republic of the philippines</a>, <a href="https://publications.waset.org/abstracts/search?q=assessment" title=" assessment"> assessment</a> </p> <a href="https://publications.waset.org/abstracts/46561/big-data-and-analytics-in-higher-education-an-assessment-of-its-status-relevance-and-future-in-the-republic-of-the-philippines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46561.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">348</span> Learning Analytics in a HiFlex Learning Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Matthew%20Montebello">Matthew Montebello</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Student engagement within a virtual learning environment generates masses of data points that can significantly contribute to the learning analytics that lead to decision support. Ideally, similar data is collected during student interaction with a physical learning space, and as a consequence, data is present at a large scale, even in relatively small classes. In this paper, we report of such an occurrence during classes held in a HiFlex modality as we investigate the advantages of adopting such a methodology. We plan to take full advantage of the learner-generated data in an attempt to further enhance the effectiveness of the adopted learning environment. This could shed crucial light on operating modalities that higher education institutions around the world will switch to in a post-COVID era. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HiFlex" title="HiFlex">HiFlex</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20higher%20education" title=" big data in higher education"> big data in higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20learning%20environment" title=" virtual learning environment"> virtual learning environment</a> </p> <a href="https://publications.waset.org/abstracts/157036/learning-analytics-in-a-hiflex-learning-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157036.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">347</span> Analytics Capabilities and Employee Role Stressors: Implications for Organizational Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Divine%20Agozie">Divine Agozie</a>, <a href="https://publications.waset.org/abstracts/search?q=Muesser%20Nat"> Muesser Nat</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Afful-Dadzie"> Eric Afful-Dadzie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This examination attempts an analysis of the effect of business intelligence and analytics (BI&A) capabilities on organizational role stressors and the implications of such an effect on performance. Two hundred twenty-eight responses gathered from seventy-six firms across Ghana were analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach to validate the hypothesized relationships identified in the research model. Findings suggest both endogenous and exogenous dependencies of the sensing capability on the multiple role requirements of personnel. Further, transforming capability increases role conflict, whereas driving capability of BI&A systems impacts role conflict and role ambiguity. This study poses many practical insights to firms seeking to acquire analytics capabilities to drive performance and data-driven decision-making. It is important for firms to consider balancing role changes and task requirements before implementing and post-implementation stages of BI&A innovations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence%20and%20analytics" title="business intelligence and analytics">business intelligence and analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20capabilities%20view" title=" dynamic capabilities view"> dynamic capabilities view</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20stressors" title=" organizational stressors"> organizational stressors</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modelling" title=" structural equation modelling"> structural equation modelling</a> </p> <a href="https://publications.waset.org/abstracts/160565/analytics-capabilities-and-employee-role-stressors-implications-for-organizational-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160565.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">346</span> Data Analytics in Energy Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanjivrao%20Katakam">Sanjivrao Katakam</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanumoorthi%20I."> Thanumoorthi I.</a>, <a href="https://publications.waset.org/abstracts/search?q=Antony%20Gerald"> Antony Gerald</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratan%20Kulkarni"> Ratan Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaju%20Nair"> Shaju Nair</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With increasing energy costs and its impact on the business, sustainability today has evolved from a social expectation to an economic imperative. Therefore, finding methods to reduce cost has become a critical directive for Industry leaders. Effective energy management is the only way to cut costs. However, Energy Management has been a challenge because it requires a change in old habits and legacy systems followed for decades. Today exorbitant levels of energy and operational data is being captured and stored by Industries, but they are unable to convert these structured and unstructured data sets into meaningful business intelligence. It must be noted that for quick decisions, organizations must learn to cope with large volumes of operational data in different formats. Energy analytics not only helps in extracting inferences from these data sets, but also is instrumental in transformation from old approaches of energy management to new. This in turn assists in effective decision making for implementation. It is the requirement of organizations to have an established corporate strategy for reducing operational costs through visibility and optimization of energy usage. Energy analytics play a key role in optimization of operations. The paper describes how today energy data analytics is extensively used in different scenarios like reducing operational costs, predicting energy demands, optimizing network efficiency, asset maintenance, improving customer insights and device data insights. The paper also highlights how analytics helps transform insights obtained from energy data into sustainable solutions. The paper utilizes data from an array of segments such as retail, transportation, and water sectors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20analytics" title="energy analytics">energy analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20management" title=" energy management"> energy management</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20data" title=" operational data"> operational data</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/8716/data-analytics-in-energy-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">364</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">345</span> Unified Structured Process for Health Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Supunmali%20Ahangama">Supunmali Ahangama</a>, <a href="https://publications.waset.org/abstracts/search?q=Danny%20Chiang%20Choon%20Poo"> Danny Chiang Choon Poo </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Health analytics (HA) is used in healthcare systems for effective decision-making, management, and planning of healthcare and related activities. However, user resistance, the unique position of medical data content, and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. The success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose an HA process model with features from the rational unified process (RUP) model and agile methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agile%20methodology" title="agile methodology">agile methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20analytics" title=" health analytics"> health analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=unified%20process%20model" title=" unified process model"> unified process model</a>, <a href="https://publications.waset.org/abstracts/search?q=UML" title=" UML"> UML</a> </p> <a href="https://publications.waset.org/abstracts/18324/unified-structured-process-for-health-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18324.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">344</span> Talent Management through Integration of Talent Value Chain and Human Capital Analytics Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wuttigrai%20Ngamsirijit">Wuttigrai Ngamsirijit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.     <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title="decision making">decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20capital%20analytics" title=" human capital analytics"> human capital analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=talent%20management" title=" talent management"> talent management</a>, <a href="https://publications.waset.org/abstracts/search?q=talent%20value%20chain" title=" talent value chain"> talent value chain</a> </p> <a href="https://publications.waset.org/abstracts/107981/talent-management-through-integration-of-talent-value-chain-and-human-capital-analytics-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107981.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">343</span> Predictive Analytics in Oil and Gas Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suchitra%20Chnadrashekhar">Suchitra Chnadrashekhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earlier looked as a support function in an organization information technology has now become a critical utility to manage their daily operations. Organizations are processing huge amount of data which was unimaginable few decades before. This has opened the opportunity for IT sector to help industries across domains to handle the data in the most intelligent manner. Presence of IT has been a leverage for the Oil & Gas industry to store, manage and process the data in most efficient way possible thus deriving the economic value in their day-to-day operations. Proper synchronization between Operational data system and Information Technology system is the need of the hour. Predictive analytics supports oil and gas companies by addressing the challenge of critical equipment performance, life cycle, integrity, security, and increase their utilization. Predictive analytics go beyond early warning by providing insights into the roots of problems. To reach their full potential, oil and gas companies need to take a holistic or systems approach towards asset optimization and thus have the functional information at all levels of the organization in order to make the right decisions. This paper discusses how the use of predictive analysis in oil and gas industry is redefining the dynamics of this sector. Also, the paper will be supported by real time data and evaluation of the data for a given oil production asset on an application tool, SAS. The reason for using SAS as an application for our analysis is that SAS provides an analytics-based framework to improve uptimes, performance and availability of crucial assets while reducing the amount of unscheduled maintenance, thus minimizing maintenance-related costs and operation disruptions. With state-of-the-art analytics and reporting, we can predict maintenance problems before they happen and determine root causes in order to update processes for future prevention. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrocarbon" title="hydrocarbon">hydrocarbon</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20technology" title=" information technology"> information technology</a>, <a href="https://publications.waset.org/abstracts/search?q=SAS" title=" SAS"> SAS</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics" title=" predictive analytics"> predictive analytics</a> </p> <a href="https://publications.waset.org/abstracts/47867/predictive-analytics-in-oil-and-gas-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">342</span> Advances in Mathematical Sciences: Unveiling the Power of Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ullah">Zahid Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Atlas%20Khan"> Atlas Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20sciences" title="mathematical sciences">mathematical sciences</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=advances" title=" advances"> advances</a>, <a href="https://publications.waset.org/abstracts/search?q=unveiling" title=" unveiling"> unveiling</a> </p> <a href="https://publications.waset.org/abstracts/167309/advances-in-mathematical-sciences-unveiling-the-power-of-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167309.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">341</span> Transforming Data into Knowledge: Mathematical and Statistical Innovations in Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ullah">Zahid Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Atlas%20Khan"> Atlas Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title="data analytics">data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20innovations" title=" mathematical innovations"> mathematical innovations</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-making" title=" decision-making"> decision-making</a> </p> <a href="https://publications.waset.org/abstracts/167376/transforming-data-into-knowledge-mathematical-and-statistical-innovations-in-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167376.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drilldown%20analytics&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drilldown%20analytics&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drilldown%20analytics&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=drilldown%20analytics&page=5">5</a></li> <li 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